{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matplotlibrcの場所"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.../site-packages/matplotlib/mpl-data/matplotlibrc'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.matplotlib_fname()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 日本語の設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T12:13:03.811478Z",
     "start_time": "2018-08-25T12:13:03.372286Z"
    }
   },
   "outputs": [],
   "source": [
    "# フォントのキャッシュを削除\n",
    "import matplotlib.font_manager as fm\n",
    "fm._rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T12:13:04.273106Z",
     "start_time": "2018-08-25T12:13:04.110691Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 日本語が表示できるかのテスト\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot([1, 3], [2, 4])\n",
    "ax.set_title('日本語のテスト')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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